Nikhil Muralidhar

According to our database, Nikhil Muralidhar authored at least 40 papers between 2015 and 2026.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2026

Finetune-Informed Pretraining Boosts Downstream Performance.

Atik Faysal, Mohammad Rostami, Reihaneh Gh. Roshan, Nikhil Muralidhar, Huaxia Wang

CoRR, January, 2026

Semi-Supervised Masked Autoencoders: Unlocking Vision Transformer Potential with Limited Data.

Atik Faysal, Mohammad Rostami, Reihaneh Gh. Roshan, Nikhil Muralidhar, Huaxia Wang

CoRR, January, 2026

DenoMAE2.0: Improving Denoising Masked Autoencoders by Classifying Local Patches for Automatic Modulation Classification.

Atik Faysal, Mohammad Rostami, Taha Boushine, Reihaneh Gh. Roshan, Nikhil Muralidhar, Yu-Dong Yao, Huaxia Wang

IEEE Trans. Commun., 2026

2025

DiSC-AMC: Token- and Parameter-Efficient Discretized Statistics In-Context Automatic Modulation Classification.

Mohammad Rostami, Atik Faysal, Reihaneh Gh. Roshan, Huaxia Wang, Nikhil Muralidhar, Yu-Dong Yao

CoRR, October, 2025

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals.

Atik Faysal, Taha Boushine, Mohammad Rostami, Reihaneh Gh. Roshan, Huaxia Wang, Nikhil Muralidhar, Avimanyu Sahoo, Yu-Dong Yao

IEEE Commun. Lett., July, 2025

Can AI Validate Science? Benchmarking LLMs for Accurate Scientific Claim → Evidence Reasoning.

Shashidhar Reddy Javaji, Yupeng Cao, Haohang Li, Yangyang Yu, Nikhil Muralidhar, Zining Zhu

CoRR, June, 2025

The Prompt is Mightier than the Example.

Shengzhe Xu, Nikhil Muralidhar, Naren Ramakrishnan

CoRR, May, 2025

Plug-and-Play AMC: Context Is King in Training-Free, Open-Set Modulation with LLMs.

Mohammad Rostami, Atik Faysal, Reihaneh Gh. Roshan, Huaxia Wang, Nikhil Muralidhar, Yu-Dong Yao

CoRR, May, 2025

DenoMAE2.0: Improving Denoising Masked Autoencoders by Classifying Local Patches.

Atik Faysal, Mohammad Rostami, Taha Boushine, Reihaneh Gh. Roshan, Huaxia Wang, Nikhil Muralidhar

CoRR, February, 2025

Can AI Validate Science? Benchmarking LLMs on Claim →Evidence Reasoning in AI Papers.

Shashidhar Reddy Javaji, Yupeng Cao, Haohang Li, Yangyang Yu, Nikhil Muralidhar, Zining Zhu

Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, 2025

Model-Agnostic Knowledge Guided Correction for Improved Neural Surrogate Rollout.

Bharat Srikishan, Daniel O'Malley, Mohamed Mehana, Nicholas Lubbers, Nikhil Muralidhar

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

LLM-Modulo-Rec: Leveraging Approximate World Knowledge of LLMs to Improve eCommerce Search Ranking Under Data Paucity.

Ali El Sayed, Reihaneh Gh. Roshan, Sathappan Muthiah, Nikhil Muralidhar

Proceedings of the IEEE International Conference on Big Data, 2025

2024

Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems.

Shengzhe Xu, Christo Kurisummoottil Thomas, Omar Hashash, Nikhil Muralidhar, Walid Saad, Naren Ramakrishnan

IEEE Netw., September, 2024

Are LLMs Naturally Good at Synthetic Tabular Data Generation?

Shengzhe Xu, Cho-Ting Lee, Mandar Sharma, Raquib Bin Yousuf, Nikhil Muralidhar, Naren Ramakrishnan

CoRR, 2024

Information Guided Regularization for Fine-tuning Language Models.

Mandar Sharma, Nikhil Muralidhar, Shengzhe Xu, Raquib Bin Yousuf, Naren Ramakrishnan

CoRR, 2024

NMformer: A Transformer for Noisy Modulation Classification in Wireless Communication.

Atik Faysal, Mohammad Rostami, Reihaneh Gh. Roshan, Huaxia Wang, Nikhil Muralidhar

Proceedings of the 33rd Wireless and Optical Communications Conference, 2024

Laying Anchors: Semantically Priming Numerals in Language Modeling.

Mandar Sharma, Rutuja Murlidhar Taware, Pravesh Koirala, Nikhil Muralidhar, Naren Ramakrishnan

Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2024, 2024

Counter Data Paucity through Adversarial Invariance Encoding: A Case Study on Modeling Battery Thermal Runaway.

Anika Tabassum, Srikanth Allu, Ramakrishnan Kannan, Nikhil Muralidhar

Proceedings of the IEEE International Conference on Big Data, 2024

Reinforcement Learning as a Parsimonious Alternative to Prediction Cascades: A Case Study on Image Segmentation.

Bharat Srikishan, Anika Tabassum, Srikanth Allu, Ramakrishnan Kannan, Nikhil Muralidhar

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

ML-Assisted Optimization of Securities Lending.

Abhinav Prasad, Prakash Arunachalam, Ali Motamedi, Ranjeeta Bhattacharya, Beibei Liu, Hays McCormick, Shengzhe Xu, Nikhil Muralidhar, Naren Ramakrishnan

Proceedings of the 4th ACM International Conference on AI in Finance, 2023

Learning Non-linguistic Skills without Sacrificing Linguistic Proficiency.

Mandar Sharma, Nikhil Muralidhar, Naren Ramakrishnan

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

2022

Science Guided Machine Learning: Incorporating Scientific Domain Knowledge for Learning Under Data Paucity and Noisy Contexts.

Nikhil Muralidhar

PhD thesis, 2022

Overcoming Barriers to Skill Injection in Language Modeling: Case Study in Arithmetic.

Mandar Sharma, Nikhil Muralidhar, Naren Ramakrishnan

CoRR, 2022

Efficient Generative Wireless Anomaly Detection for Next Generation Networks.

Gopikrishna Rathinavel, Nikhil Muralidhar, Naren Ramakrishnan, Timothy J. O'Shea

Proceedings of the IEEE Military Communications Conference, 2022

Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback.

Gopikrishna Rathinavel, Nikhil Muralidhar, Timothy J. O'Shea, Naren Ramakrishnan

Proceedings of the IEEE International Conference on Data Mining, 2022

MatPhase: Material phase prediction for Li-ion Battery Reconstruction using Hierarchical Curriculum Learning.

Anika Tabassum, Nikhil Muralidhar, Ramakrishnan Kannan, Srikanth Allu

Proceedings of the IEEE International Conference on Big Data, 2022

2021

Using AntiPatterns to avoid MLOps Mistakes.

Nikhil Muralidhar, Sathappan Muthiah, Patrick Butler, Manish Jain, Yu Yu, Katy Burne, Weipeng Li, David Jones, Prakash Arunachalam, Hays 'Skip' McCormick, Naren Ramakrishnan

CoRR, 2021

PhyFlow: Physics-Guided Deep Learning for Generating Interpretable 3D Flow Fields.

Nikhil Muralidhar, Jie Bu, Ze Cao, Neil Raj, Naren Ramakrishnan, Danesh K. Tafti, Anuj Karpatne

Proceedings of the IEEE International Conference on Data Mining, 2021

Contrastive Graph Convolutional Networks for Hardware Trojan Detection in Third Party IP Cores.

Nikhil Muralidhar, Abdullah Zubair, Nathanael R. Weidler, Ryan M. Gerdes, Naren Ramakrishnan

Proceedings of the IEEE International Symposium on Hardware Oriented Security and Trust, 2021

Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19.

Alexander Rodríguez, Nikhil Muralidhar, Bijaya Adhikari, Anika Tabassum, Naren Ramakrishnan, B. Aditya Prakash

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Cut-n-Reveal: Time Series Segmentations with Explanations.

Nikhil Muralidhar, Anika Tabassum, Liangzhe Chen, Supriya Chinthavali, Naren Ramakrishnan, B. Aditya Prakash

ACM Trans. Intell. Syst. Technol., 2020

Physics-Guided Deep Learning for Drag Force Prediction in Dense Fluid-Particulate Systems.

Nikhil Muralidhar, Jie Bu, Ze Cao, Long He, Naren Ramakrishnan, Danesh K. Tafti, Anuj Karpatne

Big Data, 2020

PhyNet: Physics Guided Neural Networks for Particle Drag Force Prediction in Assembly.

Nikhil Muralidhar, Jie Bu, Ze Cao, Long He, Naren Ramakrishnan, Danesh K. Tafti, Anuj Karpatne

Proceedings of the 2020 SIAM International Conference on Data Mining, 2020

2019

Physics-guided Design and Learning of Neural Networks for Predicting Drag Force on Particle Suspensions in Moving Fluids.

Nikhil Muralidhar, Jie Bu, Ze Cao, Long He, Naren Ramakrishnan, Danesh K. Tafti, Anuj Karpatne

CoRR, 2019

DyAt Nets: Dynamic Attention Networks for State Forecasting in Cyber-Physical Systems.

Nikhil Muralidhar, Sathappan Muthiah, Naren Ramakrishnan

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Detection of False Data Injection Attacks in Cyber-Physical Systems using Dynamic Invariants.

Kiyoshi Nakayama, Nikhil Muralidhar, Chenrui Jin, Ratnesh Sharma

Proceedings of the 18th IEEE International Conference On Machine Learning And Applications, 2019

Multivariate Long-Term State Forecasting in Cyber-Physical Systems: A Sequence to Sequence Approach.

Nikhil Muralidhar, Sathappan Muthiah, Kiyoshi Nakayama, Ratnesh Sharma, Naren Ramakrishnan

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2018

illiad: InteLLigent Invariant and Anomaly Detection in Cyber-Physical Systems.

Nikhil Muralidhar, Chen Wang, Nathan Self, Marjan Momtazpour, Kiyoshi Nakayama, Ratnesh Sharma, Naren Ramakrishnan

ACM Trans. Intell. Syst. Technol., 2018

Incorporating Prior Domain Knowledge into Deep Neural Networks.

Nikhil Muralidhar, Mohammad Raihanul Islam, Manish Marwah, Anuj Karpatne, Naren Ramakrishnan

Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

2015

Recommending Temporally Relevant News Content from Implicit Feedback Data.

Nikhil Muralidhar, Huzefa Rangwala, Eui-Hong Sam Han

Proceedings of the 27th IEEE International Conference on Tools with Artificial Intelligence, 2015